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In this study, working with the task of object retrieval in clutter, we havedeveloped a robot learning framework in which Monte Carlo Tree Search (MCTS) isfirst applied to enable a Deep Neural Network (DNN) to learn the intricateinteractions between a robot arm and a complex scene containing many objects,allowing the DNN to partially clone the behavior of MCTS. In turn, the trainedDNN is integrated into MCTS to help guide its search effort. We call thisapproach learning-guided Monte Carlo tree search for Object REtrieval (MORE),which delivers significant computational efficiency gains and added solutionoptimality. MORE is a self-supervised robotics framework/pipeline capable ofworking in the real world that successfully embodies the System 2 to System 1learning philosophy proposed by Kahneman, where learned knowledge, usedproperly, can help greatly speed up a time-consuming decision process overtime. Videos and supplementary material can be found athttps://github.com/arc-l/more

Interleaving Monte Carlo Tree Search and Self-Supervised Learning for Object Retrieval in Clutter